This sections show the processing times values for the generated instances.
The plot below shows the processing times density plot for each of the generated distributions.
The plot below shows the processing times for two machines on instances with different correlation values.
Algoritm parameter details:
| param | type | values |
|---|---|---|
| IOR | o | 0 0.25 0.5 0.75 1 |
| IOI | c | sum_pij dev_pij avgdev_pij abs_dif ss_sra ss_srs ss_srn_rcn ss_sra_rcn ss_srs_rcn ss_sra_2rcn ra_c1 ra_c2 ra_c3 lr_it_aj_ct lr_it_ct lr_it lr_aj lr_ct kk1 kk2 nm |
| IOW | c | no yes |
| IOS | c | incr decr hill valley hi_hilo hi_lohi lo_hilo lo_lohi |
| NOI | c | sum_pij dev_pij avgdev_pij abs_dif ss_sra ss_srs ss_srn_rcn ss_sra_rcn ss_srs_rcn ss_sra_2rcn ra_c1 ra_c2 ra_c3 lr_it_ct lr_it lr_aj lr_ct kk1 kk2 nm |
| NOS | c | incr decr hill valley hi_hilo hi_lohi lo_hilo lo_lohi |
| NOW | c | no yes |
| NTB | c | first_best last_best kk1 kk2 nm1 |
The plots below show the histograms for all features colored by: problem type, objective, number of jobs, number of machines and correlation type (for generated instances).
The sections below show the distributions of the best values for each parameters colored by the objective, problem type, number of jobs and number of machines.
Accuracy for each recommendation model and parameter:
F-score for each recommendation model and parameter:
The plots below show the decision trees generated for each parameter task.
Bellow are all the decision trees for the recommendation tasks considering parameter dependencies:
The bar plots below show, for each parameter, the variable importance for the decision tree model without dependencies:
For the models including parameter dependencies, the variances importance are shown below:
The optimization performance considered the average relative performance \((perf - best\_perf) / best\_perf\).
Th quantiles for the performance values for each model are shown in the table below.
| model_strat | q00 | q25 | q50 | q75 | q100 |
|---|---|---|---|---|---|
| Random | -9.319826 | 1.0872599 | 4.5165265 | 15.9898617 | 2137.01230 |
| Standard NEH | -8.489846 | 0.0000000 | 0.4352138 | 1.6293360 | 28.46078 |
| Global-best NEH | -9.913169 | 0.0000000 | 0.4488229 | 1.4098469 | 14.31729 |
| DT | -8.911596 | 0.0000000 | 0.1750807 | 0.8874725 | 13.32882 |
| DT+Dependencies | -8.565194 | 0.0000000 | 0.1765974 | 0.8709982 | 10.72643 |
| RF | -8.489846 | 0.0000000 | 0.1549657 | 0.8165678 | 24.01786 |
| RF+Dependencies | -8.367690 | -0.0083383 | 0.0975155 | 0.6451000 | 16.71320 |
Below is the violin plot for each model performance.
And filtering the random choice performance:
The Friedman test was used on the optimization performance data considering each instance as a block. The table below shows the test p-values adjusted with Nemenyi post-hoc:
| Random | Standard NEH | Global-best NEH | DT | DT+Dependencies | RF | |
|---|---|---|---|---|---|---|
| Standard NEH | 0 | NA | NA | NA | NA | NA |
| Global-best NEH | 0 | 0.3105267 | NA | NA | NA | NA |
| DT | 0 | 0.0000000 | 0 | NA | NA | NA |
| DT+Dependencies | 0 | 0.0000000 | 0 | 0.9995260 | NA | NA |
| RF | 0 | 0.0000000 | 0 | 0.9999419 | 0.9908703 | NA |
| RF+Dependencies | 0 | 0.0000000 | 0 | 0.0004696 | 0.0000655 | 0.0016537 |
The following test compares RF with dependencies model (the one with the best performance) against all other models considering the optimization performance. It uses the many-to-one Friedman test with Demsar post-hoc.
| RF+Dependencies | |
|---|---|
| Random | 0.00e+00 |
| Standard NEH | 0.00e+00 |
| Global-best NEH | 0.00e+00 |
| DT | 4.70e-05 |
| DT+Dependencies | 9.60e-06 |
| RF | 8.53e-05 |